{"title":"财务会计风险评估:违约概率建模","authors":"Tobias Filusch","doi":"10.1108/JRF-02-2020-0033","DOIUrl":null,"url":null,"abstract":"\nPurpose\nThis paper aims to introduce and tests models for point-in-time probability of default (PD) term structures as required by international accounting standards. Corresponding accounting standards prescribe that expected credit losses (ECLs) be recognized for the impairment of financial instruments, for which the probability of default strongly embodies the included default risk. This paper fills the research gap resulting from a lack of models that expand upon existing risk management techniques, link PD term structures of different risk classes and are compliant with accounting standards, e.g. offering the flexibility for business cycle-related variations.\n\n\nDesign/methodology/approach\nThe author modifies the non-homogeneous continuous-time Markov chain model (NHCTMCM) by Bluhm and Overbeck (2007a, 2007b) and introduces the generalized through-the-cycle model (GTTCM), which generalizes the homogeneous Markov chain approach to a point-in-time model. As part of the overall ECL estimation, an empirical study using Standard and Poor’s (S&P) transition data compares the performance of these models using the mean squared error.\n\n\nFindings\nThe models can reflect observed PD term structures associated with different time periods. The modified NHCTMCM performs best at the expense of higher complexity and only its cumulative PD term structures can be transferred to valid ECL-relevant unconditional PD term structures. For direct calibration to these unconditional PD term structures, the GTTCM is only slightly worse. Moreover, it requires only half of the number of parameters that its competitor does. Both models are useful additions to the implementation of accounting regulations.\n\n\nResearch limitations/implications\nThe tests are only carried out for 15-year samples within a 35-year span of available S&P transition data. Furthermore, a point-in-time forecast of the PD term structure requires a link to the business cycle, which seems difficult to find, but is in principle necessary corresponding to the accounting requirements.\n\n\nPractical implications\nResearch findings are useful for practitioners, who apply and develop the ECL models of financial accounting.\n\n\nOriginality/value\nThe innovative models expand upon the existing methodologies for assessing financial risks, motivated by the practical requirements of new financial accounting standards.\n","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":" ","pages":""},"PeriodicalIF":5.7000,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Risk assessment for financial accounting: modeling probability of default\",\"authors\":\"Tobias Filusch\",\"doi\":\"10.1108/JRF-02-2020-0033\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nPurpose\\nThis paper aims to introduce and tests models for point-in-time probability of default (PD) term structures as required by international accounting standards. Corresponding accounting standards prescribe that expected credit losses (ECLs) be recognized for the impairment of financial instruments, for which the probability of default strongly embodies the included default risk. This paper fills the research gap resulting from a lack of models that expand upon existing risk management techniques, link PD term structures of different risk classes and are compliant with accounting standards, e.g. offering the flexibility for business cycle-related variations.\\n\\n\\nDesign/methodology/approach\\nThe author modifies the non-homogeneous continuous-time Markov chain model (NHCTMCM) by Bluhm and Overbeck (2007a, 2007b) and introduces the generalized through-the-cycle model (GTTCM), which generalizes the homogeneous Markov chain approach to a point-in-time model. As part of the overall ECL estimation, an empirical study using Standard and Poor’s (S&P) transition data compares the performance of these models using the mean squared error.\\n\\n\\nFindings\\nThe models can reflect observed PD term structures associated with different time periods. The modified NHCTMCM performs best at the expense of higher complexity and only its cumulative PD term structures can be transferred to valid ECL-relevant unconditional PD term structures. For direct calibration to these unconditional PD term structures, the GTTCM is only slightly worse. Moreover, it requires only half of the number of parameters that its competitor does. Both models are useful additions to the implementation of accounting regulations.\\n\\n\\nResearch limitations/implications\\nThe tests are only carried out for 15-year samples within a 35-year span of available S&P transition data. Furthermore, a point-in-time forecast of the PD term structure requires a link to the business cycle, which seems difficult to find, but is in principle necessary corresponding to the accounting requirements.\\n\\n\\nPractical implications\\nResearch findings are useful for practitioners, who apply and develop the ECL models of financial accounting.\\n\\n\\nOriginality/value\\nThe innovative models expand upon the existing methodologies for assessing financial risks, motivated by the practical requirements of new financial accounting standards.\\n\",\"PeriodicalId\":46579,\"journal\":{\"name\":\"Journal of Risk Finance\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2021-01-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Risk Finance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/JRF-02-2020-0033\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Risk Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/JRF-02-2020-0033","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
Risk assessment for financial accounting: modeling probability of default
Purpose
This paper aims to introduce and tests models for point-in-time probability of default (PD) term structures as required by international accounting standards. Corresponding accounting standards prescribe that expected credit losses (ECLs) be recognized for the impairment of financial instruments, for which the probability of default strongly embodies the included default risk. This paper fills the research gap resulting from a lack of models that expand upon existing risk management techniques, link PD term structures of different risk classes and are compliant with accounting standards, e.g. offering the flexibility for business cycle-related variations.
Design/methodology/approach
The author modifies the non-homogeneous continuous-time Markov chain model (NHCTMCM) by Bluhm and Overbeck (2007a, 2007b) and introduces the generalized through-the-cycle model (GTTCM), which generalizes the homogeneous Markov chain approach to a point-in-time model. As part of the overall ECL estimation, an empirical study using Standard and Poor’s (S&P) transition data compares the performance of these models using the mean squared error.
Findings
The models can reflect observed PD term structures associated with different time periods. The modified NHCTMCM performs best at the expense of higher complexity and only its cumulative PD term structures can be transferred to valid ECL-relevant unconditional PD term structures. For direct calibration to these unconditional PD term structures, the GTTCM is only slightly worse. Moreover, it requires only half of the number of parameters that its competitor does. Both models are useful additions to the implementation of accounting regulations.
Research limitations/implications
The tests are only carried out for 15-year samples within a 35-year span of available S&P transition data. Furthermore, a point-in-time forecast of the PD term structure requires a link to the business cycle, which seems difficult to find, but is in principle necessary corresponding to the accounting requirements.
Practical implications
Research findings are useful for practitioners, who apply and develop the ECL models of financial accounting.
Originality/value
The innovative models expand upon the existing methodologies for assessing financial risks, motivated by the practical requirements of new financial accounting standards.
期刊介绍:
The Journal of Risk Finance provides a rigorous forum for the publication of high quality peer-reviewed theoretical and empirical research articles, by both academic and industry experts, related to financial risks and risk management. Articles, including review articles, empirical and conceptual, which display thoughtful, accurate research and be rigorous in all regards, are most welcome on the following topics: -Securitization; derivatives and structured financial products -Financial risk management -Regulation of risk management -Risk and corporate governance -Liability management -Systemic risk -Cryptocurrency and risk management -Credit arbitrage methods -Corporate social responsibility and risk management -Enterprise risk management -FinTech and risk -Insurtech -Regtech -Blockchain and risk -Climate change and risk